Lecture_10_13 - Lecture of Oct 13 Chapter 4 Basic Methods...

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Unformatted text preview: Lecture of Oct 13 Chapter 4 Basic Methods of Statistical Process Control 1 SPC Needs and Tools In order for products to meet customer's requirements or our design intents, they have to be produced by a process that is stable or repeatable. In statistical language, the process must be capable of operating with little variability around the target or nominal values of the product's quality characteristic. Statistical Process Control (SPC) is a collection of problemsolving tools for variation reduction. Seven major SPC tools, called 7M or "magnificent seven". 2 Magnificent SEVEN 1. 2. 3. 4. 5. 6. 7. Histogram Check Sheet Pareto Chart Cause and Effect Diagram Defect Concentration Diagram Scatter Diagram Control Chart Read Section 4-4, pp. 177-184 of the 4th edition or pp. 169-174 of the 5th edition. Among the list, the first six techniques are intuitive and easy to understand. Control chart is the most technically sophisticated so we will spend the majority of our time on control charts. 3 Check Sheet 4 Pareto Charts (1) 5 Pareto Charts (2) 6 3.9 1.7 actual target Registration error Cause-and-effect diagram (a.k.a. Fishbone diagram) screen stretch rotation non-uniformity flat thickness horizontal misplacement Mis-registration in electronics assembly vertical misplacement solder pasting locating fences 7 Defect Concentration Diagram 8 Scatter Diagram 9 Basic Concepts of Control Chart The underlying philosophy behind control charts is that the causes of process variability can be broken down into two types: (1) Common causes (a.k.a. chance causes) -- inherent, unavoidable, completely random, natural causes. (2) Assignable causes (a.k.a. special causes) -- more systematic, less random, avoidable, unnatural causes. 10 Basic Concepts of Control Chart The Thoughts Behind Common cause variability Assignable cause variability 11 26 24 22 Common causes vs. Assignable causes iid data (stable mean) 0 50 100 150 x 20 18 16 14 26 24 22 x 20 18 16 14 26 24 22 Complicated dynamic changes in process non-iid data (wandering mean) 0 50 100 150 non-iid data (abruptly shifting mean) x 20 18 16 14 Simple patterns 0 50 100 150 observation number In 314, we focus on detecting the simple pattern, i.e., abrupt mean and variance change. INEN 614 studies the complex dynamic pattern. 12 Chance and assignable causes of variation Textbook Fig. 4-1 13 In-control vs out-of-control A process is considered "in-control" when only common causes exist; while the process is considered "out-of-control" when assignable causes are present. Control charts are the tool one would use to identify when a process is out of control, and understand the nature of the assignable causes. 14 A Five-Step Procedure (1) Periodically and regularly collect samples of data; (2) From each sample, calculate one or more qualityrelated statistic; (3) Construct a chart of the sample statistic versus the sample sequence number; (4) Add control limits to the chart, which are designed to indicate when the process is out of control from a statistically significant point of view; (5) Inspect the control chart to determine if the process is out of control, and if so, what is the nature of the assignable causes. 15 Control Chart Concepts The calculated statistic: mean The calculated statistic: range/variance 16 ...
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This note was uploaded on 02/14/2011 for the course ISEN 314 taught by Professor M during the Fall '10 term at Texas A&M.

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